import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate_v1 import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)

/hpc/group/pbenfeylab/ch416/miniconda3/envs/genesys/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

## Conda Env pytorch-gpu on DCC
print(torch.__version__)
print(sc.__version__) 

1.11.0
1.9.6

## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data

with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
dataset = Root_Dataset(data['X_test'], data['y_test'])
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)

input_size = data['X_train'].shape[1]
## 10 cell types 
output_size = 10
embedding_dim = 256
hidden_dim = 256
n_layers = 2
device = "cpu"
path = "./"

Load trained GeneSys model

model = ClassifierLSTM(input_size, output_size, embedding_dim, hidden_dim, n_layers).to(device)
model.load_state_dict(torch.load(path+"/workstation/genesys_model_trained_on_root_atlas_20240308_continue4.pth", map_location=torch.device('cpu')))
model = model
model.eval()

ClassifierLSTM(
  (fc1): Sequential(
    (0): Linear(in_features=17513, out_features=256, bias=True)
    (1): Dropout(p=0.2, inplace=False)
    (2): GaussianNoise()
  )
  (fc): Sequential(
    (0): ReLU()
    (1): Linear(in_features=512, out_features=512, bias=True)
    (2): ReLU()
    (3): Linear(in_features=512, out_features=10, bias=True)
  )
  (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.2, bidirectional=True)
  (dropout): Dropout(p=0.2, inplace=False)
  (b_to_z): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (bz2_infer_z1): DBlock(
    (fc1): Linear(in_features=1024, out_features=256, bias=True)
    (fc2): Linear(in_features=1024, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z1_to_z2): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z_to_x): Decoder(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=256, out_features=256, bias=True)
    (fc3): Linear(in_features=256, out_features=17513, bias=True)
  )
)

classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}

cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000

## GRN for the transition t3 to t5
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            pred_h = model.init_hidden(batch_size)
            tfrom = model.generate_next(x, pred_h, 2).to('cpu').detach().numpy()
            cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            
            pred_h = model.init_hidden(batch_size)
            tto = model.generate_next(x, pred_h, 4).to('cpu').detach().numpy()   
            cto = tto[np.where(np.array(y_label)==ct)[0],:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm

Atrichoblast
Trichoblast
Cortex
Endodermis
Pericycle
Procambium
Xylem
Phloem
Lateral Root Cap
Columella

# Save the array to disk
np.save('genesys_ctw_t3-t5.npy', ctw)

ctw = np.load('genesys_ctw_t3-t5.npy')

## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])

## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list

wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")

## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"

pd.Series(wanted_TFs['Name']).value_counts().head(5)

Name
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: count, dtype: int64

Network analysis

TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))

def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)

atri = network(0)

tri = network(1)

cor = network(2)

end = network(3)

per = network(4)

pro = network(5)

xyl = network(6)

phl = network(7)

lrc = network(8)

col = network(9)

atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']

## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts())
tf_occurance = tf_occurance.rename(columns={
    'count': 'tf_occurance'
})
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)

## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)

	atri_betweenness_centrality	tri_betweenness_centrality	lrc_betweenness_centrality	atri_out_centrality	tri_out_centrality	lrc_out_centrality	atri_in_centrality	tri_in_centrality	lrc_in_centrality	centrality_count	centrality_sum
ATS	0.878995	0.817592	0.813492	0.169620	0.044968	0.248052	0.068354	0.167024	0.255844	9	12.463941
TMO7	0.956564	0.015568	0.518173	0.134177	0.014989	0.119481	0.068354	0.404711	0.066234	9	11.298251

## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)

	atri_betweenness_centrality	tri_betweenness_centrality	atri_out_centrality	tri_out_centrality	atri_in_centrality	tri_in_centrality	centrality_count	centrality_sum
AT3G53370	0.367615	0.971800	0.050633	0.847966	0.086076	0.194861	6	8.518951
RSL4	0.013468	0.748077	0.025316	0.708779	0.015190	0.062099	6	7.572929
RHD6	0.063638	0.792567	0.020253	0.447537	0.063291	0.182013	6	7.569300
FIT	0.954495	0.049558	0.293671	0.036403	0.131646	0.100642	6	7.566414
CRF4	0.937358	0.001705	0.091139	0.014989	0.124051	0.145610	6	7.314852
AT2G28710	0.005301	0.000758	0.359494	0.053533	0.017722	0.027837	6	6.464645
AT3G09735	0.001992	0.000018	0.030380	0.119914	0.207595	0.029979	6	6.389878
WRKY61	0.000263	0.001452	0.032911	0.107066	0.015190	0.053533	6	6.210416
OFP12	0.000739	0.000758	0.035443	0.008565	0.037975	0.017131	6	6.100611

## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	atri_betweenness_centrality	atri_out_centrality	atri_in_centrality	centrality_count	centrality_sum
GL2	0.985575	0.415190	0.301266	3	4.702030
BNQ3	0.976907	0.139241	0.156962	3	4.273109
TTG2	0.969215	0.769620	0.131646	3	4.870481
TRY	0.949579	0.093671	0.124051	3	4.167301
AT1G11490	0.910711	0.020253	0.167089	3	4.098053
MEA	0.897873	0.230380	0.027848	3	4.156101
OFP15	0.622978	0.005063	0.093671	3	3.721712
AT4G31650	0.074921	0.146835	0.073418	3	3.295174
BEH4	0.071754	0.050633	0.015190	3	3.137576
MYB45	0.057052	0.554430	0.032911	3	3.644394
WRKY45	0.024880	0.258228	0.032911	3	3.316019
DAR7	0.023794	0.040506	0.007595	3	3.071895
OFP18	0.018576	0.286076	0.078481	3	3.383133
NAC78	0.010454	0.081013	0.043038	3	3.134505
WRKY47	0.008822	0.030380	0.002532	3	3.041734
AT2G27580	0.007672	0.015190	0.048101	3	3.070963
MYB64	0.007434	0.002532	0.012658	3	3.022624
HB4	0.002532	0.005063	0.022785	3	3.030380
HB24	0.000707	0.230380	0.020253	3	3.251340
NF-YC10	0.000051	0.086076	0.121519	3	3.207646
BZS1	0.000051	0.005063	0.022785	3	3.027900
AT3G17100	0.000039	0.068354	0.096203	3	3.164596
AT2G18670	0.000032	0.134177	0.020253	3	3.154463
NPH4	0.000026	0.017722	0.037975	3	3.055722
AT4G28030	0.000006	0.035443	0.002532	3	3.037981
FRS1	0.000006	0.010127	0.002532	3	3.012665

## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	tri_betweenness_centrality	tri_out_centrality	tri_in_centrality	centrality_count	centrality_sum
ESE3	0.985291	0.107066	0.239829	3	4.332186
RL6	0.984450	0.137045	0.074946	3	4.196442
AT1G02040	0.896118	0.042827	0.055675	3	3.994619
LRL3	0.889418	0.732334	0.117773	3	4.739525
MBF1A	0.886142	0.006424	0.357602	3	4.250168
RSL1	0.885462	0.111349	0.100642	3	4.097453
RAP2.7	0.819926	0.010707	0.019272	3	3.849905
ZF1	0.810704	0.079229	0.079229	3	3.969162
ATMYC1	0.772211	0.203426	0.408994	3	4.384630
EGL3	0.573761	0.053533	0.158458	3	3.785752
AT4G09100	0.484946	0.728051	0.081370	3	4.294368
MED6	0.432773	0.032120	0.021413	3	3.486307
NPR4	0.258705	0.008565	0.029979	3	3.297249
PHE1	0.188441	0.012848	0.008565	3	3.209855
AT5G04390	0.150890	0.074946	0.006424	3	3.232261
AT4G39160	0.123930	0.494647	0.029979	3	3.648556
bZIP2	0.110136	0.070664	0.139186	3	3.319986
AT5G12850	0.106961	0.006424	0.014989	3	3.128374
RSL2	0.017489	0.229122	0.006424	3	3.253035
NAC005	0.013666	0.010707	0.107066	3	3.131439
AT3G05860	0.007508	0.077088	0.008565	3	3.093162
AT3G51470	0.005041	0.014989	0.027837	3	3.047867
AT5G11340	0.000639	0.051392	0.055675	3	3.107705
HFR1	0.000625	0.059957	0.029979	3	3.090561
AT2G05160	0.000588	0.291221	0.012848	3	3.304657
RAP2.11	0.000482	0.044968	0.017131	3	3.062581
AT5G16470	0.000028	0.042827	0.113490	3	3.156344
KAN	0.000009	0.014989	0.062099	3	3.077097
AT1G04850	0.000009	0.032120	0.042827	3	3.074956
GL3	0.000005	0.119914	0.027837	3	3.147756

## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	lrc_betweenness_centrality	lrc_out_centrality	lrc_in_centrality	centrality_count	centrality_sum
CRF2	0.977525	0.485714	0.663636	3	5.126876
3xHMG-box2	0.972707	0.271429	0.381818	3	4.625954
BHLH101	0.933266	0.097403	0.197403	3	4.228072
AT1G77200	0.918153	0.072727	0.157143	3	4.148023
RGL2	0.910315	0.124675	0.203896	3	4.238887
RITF1	0.571837	0.274026	0.132468	3	3.978331
HDG1	0.543788	0.083117	0.101299	3	3.728203
ERF9	0.522338	0.211688	0.211688	3	3.945715
OFP7	0.471706	0.033766	0.062338	3	3.567809
LBD4	0.453937	0.123377	0.077922	3	3.655236
CRF11	0.431295	0.059740	0.045455	3	3.536490
AT2G36930	0.413595	0.153247	0.093506	3	3.660348
WRKY42	0.401435	0.068831	0.081818	3	3.552085
HMGB4	0.351240	0.074026	0.084416	3	3.509682
SDG4	0.350435	0.036364	0.138961	3	3.525760
HB-2	0.347194	0.119481	0.076623	3	3.543298
MYB3R-4	0.346647	0.066234	0.081818	3	3.494699
GRF2	0.339344	0.127273	0.070130	3	3.536747
IAA33	0.338063	0.029870	0.031169	3	3.399102
ARF8	0.333974	0.110390	0.027273	3	3.471636
GATA17L	0.319665	0.118182	0.075325	3	3.513171
FEZ	0.315569	0.153247	0.185714	3	3.654530
CHR1	0.277476	0.081818	0.067532	3	3.426827
PS1	0.273026	0.050649	0.048052	3	3.371727
CSDP1	0.268927	0.093506	0.049351	3	3.411785
3xHMG-box1	0.197599	0.032468	0.048052	3	3.278118
WRKY7	0.186082	0.023377	0.024675	3	3.234134
HTA2	0.174330	0.055844	0.125974	3	3.356148
GATA7	0.102602	0.042857	0.068831	3	3.214291
HTA13	0.083759	0.079221	0.098701	3	3.261681
RR10	0.052406	0.064935	0.049351	3	3.166691
CHR38	0.023318	0.070130	0.029870	3	3.123318
MYB51	0.020747	0.023377	0.024675	3	3.068799
AT2G29065	0.009236	0.067532	0.061039	3	3.137808
FLP	0.008203	0.025974	0.029870	3	3.064047
JMJ18	0.007027	0.010390	0.074026	3	3.091443
AT5G12980	0.005963	0.050649	0.038961	3	3.095574
GATA17	0.005840	0.067532	0.063636	3	3.137009
AT2G41710	0.001672	0.028571	0.025974	3	3.056217
AT2G36026	0.001647	0.037662	0.084416	3	3.123725
HMGB5	0.001569	0.025974	0.041558	3	3.069101
BRM	0.000802	0.049351	0.067532	3	3.117685
CIB5	0.000735	0.084416	0.031169	3	3.116319
HDG2	0.000483	0.048052	0.077922	3	3.126457
WRI1	0.000358	0.025974	0.033766	3	3.060098
SPL1	0.000086	0.007792	0.079221	3	3.087099
AT2G35605	0.000027	0.022078	0.033766	3	3.055871
NAC063	0.000017	0.011688	0.045455	3	3.057160
AP2	0.000008	0.042857	0.020779	3	3.063645

## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	col_betweenness_centrality	col_out_centrality	col_in_centrality	centrality_count	centrality_sum
IAA20	0.991821	0.374365	0.360406	3	4.726593
NTT	0.991046	0.175127	0.455584	3	4.621756
AT3G60670	0.983727	0.008883	0.427665	3	4.420275
BPC7	0.976530	0.024112	0.063452	3	4.064094
AT3G52440	0.894872	0.324873	0.111675	3	4.331420
...	...	...	...	...	...
AT2G44430	0.000011	0.034264	0.048223	3	3.082499
HSFA8	0.000011	0.013959	0.029188	3	3.043158
DRIP2	0.000008	0.062183	0.021574	3	3.083764
AT4G22360	0.000005	0.035533	0.030457	3	3.065995
AT3G52250	0.000003	0.036802	0.087563	3	3.124369

72 rows × 5 columns

## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)

	cor_betweenness_centrality	end_betweenness_centrality	cor_out_centrality	end_out_centrality	cor_in_centrality	end_in_centrality	centrality_count	centrality_sum
JKD	0.035369	0.006561	0.426914	0.169399	0.252900	0.137705	6	7.028849
ETR2	0.000281	0.038321	0.148492	0.013115	0.088167	0.001093	6	6.289468

## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	cor_betweenness_centrality	cor_out_centrality	cor_in_centrality	centrality_count	centrality_sum
MYB50	0.781838	0.255220	0.069606	3	4.106664
AT1G72210	0.126245	0.180974	0.294664	3	3.601883
AT2G38300	0.063519	0.575406	0.529002	3	4.167927
RGL3	0.046323	0.327146	0.273782	3	3.647251
MYB86	0.042886	0.475638	0.450116	3	3.968640
HAM3	0.025803	0.306265	0.220418	3	3.552485
LRP1	0.011601	0.320186	0.257541	3	3.589327
GATA16	0.006961	0.204176	0.169374	3	3.380510
MYBR1	0.006707	0.034803	0.053364	3	3.094874
WRKY57	0.004630	0.157773	0.248260	3	3.410662
BZR1	0.002309	0.127610	0.180974	3	3.310894
AT3G61420	0.000529	0.060325	0.078886	3	3.139740
AT3G24120	0.000237	0.174014	0.122970	3	3.297221
EIL1	0.000210	0.185615	0.076566	3	3.262391
bZIP52	0.000081	0.067285	0.039443	3	3.106809
JAZ6	0.000070	0.034803	0.106729	3	3.141601
RR3	0.000011	0.062645	0.048724	3	3.111380

## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	end_betweenness_centrality	end_out_centrality	end_in_centrality	centrality_count	centrality_sum
MYB74	0.802011	0.951913	0.100546	3	4.854470
MYB36	0.591693	0.991257	1.000000	3	5.582950
LSMT-L	0.056560	0.001093	0.002186	3	3.059839
AN3	0.022951	0.006557	0.243716	3	3.273224
SCR	0.020775	0.160656	0.217486	3	3.398917
AT5G57150	0.015549	0.451366	0.140984	3	3.607899
chr31	0.015436	0.158470	0.160656	3	3.334561
BLJ	0.015301	0.629508	0.189071	3	3.833880
AGL67	0.012107	0.003279	0.004372	3	3.019757
AT2G27930	0.011930	0.077596	0.004372	3	3.093897
HSFB4	0.005464	0.003279	0.137705	3	3.146448
MYB68	0.004473	0.781421	0.109290	3	3.895184
TLP11	0.002175	0.121311	0.087432	3	3.210918
AT3G56230	0.001820	0.718033	0.067760	3	3.787612
LAF1	0.001412	0.029508	0.072131	3	3.103052
AGL16	0.001108	0.056831	0.004372	3	3.062311
AT3G23690	0.001095	0.168306	0.088525	3	3.257926
HRS1	0.001076	0.061202	0.003279	3	3.065557
MYB122	0.000729	0.034973	0.039344	3	3.075046
NAC003	0.000122	0.001093	0.005464	3	3.006679
STO	0.000014	0.018579	0.028415	3	3.047009
AT1G48040	0.000013	0.002186	0.005464	3	3.007663
ZFN1	0.000012	0.017486	0.026230	3	3.043728
ABF2	0.000008	0.006557	0.020765	3	3.027331
MYB70	0.000006	0.013115	0.025137	3	3.038257
AT3G04930	0.000006	0.004372	0.019672	3	3.024050
FBH3	0.000006	0.007650	0.020765	3	3.028421
ERF15	0.000002	0.066667	0.018579	3	3.085248
AT5G23280	0.000001	0.038251	0.004372	3	3.042624

## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)

	per_betweenness_centrality	pro_betweenness_centrality	xyl_betweenness_centrality	phl_betweenness_centrality	per_out_centrality	pro_out_centrality	xyl_out_centrality	phl_out_centrality	per_in_centrality	pro_in_centrality	xyl_in_centrality	phl_in_centrality	centrality_count	centrality_sum
AT3G43430	0.955343	0.067799	0.002557	0.970183	0.834906	0.959057	0.039897	0.463748	0.957547	0.264268	0.043758	0.125855	12	17.684920
AT1G61660	0.000005	0.959442	0.532376	0.887866	0.075472	0.800248	0.045045	0.073871	0.006289	0.313896	0.137709	0.041040	12	15.873259
HB-8	0.001248	0.057658	0.277498	0.540318	0.003145	0.864764	0.942085	0.008208	0.017296	0.172457	0.666667	0.015048	12	15.566390
ATAUX2-11	0.652630	0.010032	0.996826	0.001392	0.066038	0.841191	0.374517	0.013680	0.037736	0.176179	0.155727	0.038304	12	15.364251
IAA12	0.066956	0.982903	0.934685	0.000187	0.004717	0.937965	0.051480	0.013680	0.007862	0.045906	0.038610	0.015048	12	15.099999
UNE12	0.008565	0.001241	0.001438	0.076272	0.355346	0.334988	0.120978	0.079343	0.251572	0.332506	0.178893	0.023256	12	13.764398

## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	per_betweenness_centrality	per_out_centrality	per_in_centrality	centrality_count	centrality_sum
SAC51	0.870519	0.094340	0.077044	3	4.041903
NAC047	0.861531	0.113208	0.039308	3	4.014047
AT2G42040	0.790041	0.055031	0.018868	3	3.863940
AT3G04850	0.654831	0.028302	0.025157	3	3.708290
HB-7	0.507594	0.265723	0.452830	3	4.226148
HMGB6	0.461145	0.006289	0.092767	3	3.560202
AT5G06550	0.177965	0.004717	0.009434	3	3.192116
GATA23	0.164480	0.048742	0.177673	3	3.390895
AT1G27050	0.053375	0.023585	0.003145	3	3.080104
IDD7	0.018125	0.018868	0.006289	3	3.043282
LBD14	0.014735	0.080189	0.022013	3	3.116937
AT3G21330	0.011826	0.003145	0.080189	3	3.095159
NUC	0.008624	0.152516	0.055031	3	3.216171
TRFL10	0.007812	0.044025	0.011006	3	3.062844
AT4G20970	0.007443	0.058176	0.015723	3	3.081343
AT2G20100	0.006364	0.154088	0.009434	3	3.169886
MGP	0.003972	0.053459	0.110063	3	3.167494
AT2G35430	0.003053	0.070755	0.015723	3	3.089531
GRP2	0.001582	0.017296	0.306604	3	3.325482
SHR	0.001572	0.055031	0.091195	3	3.147799
RSZ22	0.001572	0.031447	0.059748	3	3.092767
IAA18	0.000567	0.034591	0.006289	3	3.041448
AT1G27660	0.000245	0.127358	0.007862	3	3.135465
AT4G00940	0.000015	0.023585	0.025157	3	3.048757
CDF3	0.000007	0.029874	0.003145	3	3.033026
IDD11	0.000002	0.136792	0.072327	3	3.209122
DEL2	0.000002	0.007862	0.003145	3	3.011009

## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	pro_betweenness_centrality	pro_out_centrality	pro_in_centrality	centrality_count	centrality_sum
HAT3	0.955693	0.192308	0.018610	3	4.166611
AT5G50010	0.955669	0.697270	0.024814	3	4.677754
NAC045	0.043551	0.047146	0.008685	3	3.099382
AT3G10040	0.031677	0.012407	0.011166	3	3.055250
REV	0.023168	0.545906	0.101737	3	3.670811
AT2G29660	0.012140	0.540943	0.035980	3	3.589063
HB18	0.003656	0.115385	0.059553	3	3.178594
AT1G75490	0.001313	0.127792	0.183623	3	3.312728
AT4G17780	0.001304	0.054591	0.012407	3	3.068301
BZIP24	0.001262	0.052109	0.013648	3	3.067019
ERF12	0.001241	0.034739	0.193548	3	3.229529
AT5G24320	0.001241	0.009926	0.099256	3	3.110422
AT5G60142	0.000162	0.023573	0.002481	3	3.026216
HYH	0.000034	0.193548	0.120347	3	3.313930
GATA26	0.000032	0.016129	0.073201	3	3.089362
AT1G18960	0.000020	0.002481	0.006203	3	3.008705
BPC1	0.000015	0.003722	0.089330	3	3.093068
LUH	0.000014	0.019851	0.063275	3	3.083140
SHY2	0.000014	0.263027	0.027295	3	3.290336
OBP4	0.000006	0.220844	0.055831	3	3.276681
TLP3	0.000003	0.032258	0.069479	3	3.101740
VRN1	0.000003	0.060794	0.055831	3	3.116628
AL4	0.000002	0.007444	0.095533	3	3.102979

## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	xyl_betweenness_centrality	xyl_out_centrality	xyl_in_centrality	centrality_count	centrality_sum
MYB83	0.990722	0.737452	0.117117	3	4.845291
VND1	0.982980	0.541828	0.060489	3	4.585297
BEE2	0.982901	0.236808	0.097812	3	4.317521
XND1	0.963602	0.889318	0.184041	3	5.036961
AT4G13620	0.962645	0.050193	0.037323	3	4.050162
VND7	0.942810	0.790219	0.054054	3	4.787083
SHP1	0.933945	0.234234	0.114543	3	4.282722
LBD18	0.923374	0.079794	0.129987	3	4.133155
VND5	0.560078	0.751609	0.036036	3	4.347723
MYB46	0.323950	0.903475	0.131274	3	4.358699
VND2	0.107889	0.988417	0.343629	3	4.439936
AT1G66810	0.056570	0.182754	0.030888	3	3.270212
MYB99	0.038995	0.117117	0.002574	3	3.158686
AT2G04845	0.033839	0.223938	0.025740	3	3.283517
ARR9	0.029427	0.007722	0.010296	3	3.047445
VND4	0.016379	0.768340	0.118404	3	3.903123
AT1G26610	0.015545	0.060489	0.083655	3	3.159689
TCP10	0.014767	0.018018	0.019305	3	3.052090
BEL10	0.014401	0.046332	0.036036	3	3.096769
MYB25	0.013923	0.027027	0.045045	3	3.085995
HB31	0.008100	0.051480	0.019305	3	3.078885
AGL58	0.007686	0.006435	0.100386	3	3.114507
BZIP49	0.005297	0.023166	0.029601	3	3.058064
PHV	0.003886	0.111969	0.189189	3	3.305044
AT1G68200	0.002826	0.590734	0.033462	3	3.627022
JLO	0.002516	0.057915	0.081081	3	3.141512
IAA6	0.002476	0.886744	0.050193	3	3.939413
AT5G18090	0.002357	0.023166	0.016731	3	3.042254
SPL7	0.001285	0.048906	0.041184	3	3.091375
AT5G06770	0.000214	0.003861	0.007722	3	3.011797
TCP20	0.000071	0.151866	0.061776	3	3.213714
AT2G01818	0.000050	0.002574	0.105534	3	3.108158
VND6	0.000028	0.141570	0.042471	3	3.184069
AT1G03350	0.000020	0.063063	0.056628	3	3.119711
IWS1	0.000020	0.027027	0.052767	3	3.079814

## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	phl_betweenness_centrality	phl_out_centrality	phl_in_centrality	centrality_count	centrality_sum
NAC020	0.980417	0.221614	0.154583	3	4.356614
AT5G02460	0.974379	0.083447	0.160055	3	4.217881
GATA20	0.972391	0.105335	0.110807	3	4.188533
AT2G44940	0.950758	0.357045	0.083447	3	4.391250
AT5G41380	0.905144	0.482900	0.283174	3	4.671218
WOX2	0.850503	0.010944	0.049248	3	3.910695
APL	0.824045	0.982216	1.001368	3	5.807629
NAC080	0.650170	0.001368	0.015048	3	3.666585
AT2G03500	0.235905	0.658003	0.659371	3	4.553278
AT3G12730	0.229208	0.820793	0.838577	3	4.888578
AS1	0.161295	0.050616	0.004104	3	3.216015
CRF1	0.156860	0.071135	0.098495	3	3.326490
DAR2	0.129382	0.625171	0.766074	3	4.520627
NAC057	0.070987	0.499316	0.068399	3	3.638703
AT1G02030	0.058578	0.009576	0.020520	3	3.088674
NF-YB3	0.023882	0.005472	0.001368	3	3.030722
AT4G37180	0.021159	0.580027	0.261286	3	3.862472
HCA2	0.012876	0.396717	0.082079	3	3.491672
AT3G22100	0.011326	0.002736	0.045144	3	3.059206
DOF6	0.005933	0.685363	0.132695	3	3.823990
SPL13A	0.005030	0.009576	0.002736	3	3.017342
PIF7	0.004805	0.012312	0.024624	3	3.041741
RSZ22a	0.002374	0.005472	0.061560	3	3.069406
SVP	0.001578	0.099863	0.001368	3	3.102809
WRKY32	0.001327	0.186047	0.097127	3	3.284500
REM22	0.001327	0.041040	0.091655	3	3.134022
RAP2.2	0.001081	0.031464	0.058824	3	3.091369
AT1G63820	0.000944	0.310534	0.139535	3	3.451013
VOZ1	0.000723	0.195622	0.077975	3	3.274321
PRT1	0.000588	0.084815	0.028728	3	3.114132
NAC2	0.000515	0.213406	0.035568	3	3.249489
bZIP19	0.000386	0.184679	0.158687	3	3.343751
ET2	0.000350	0.031464	0.010944	3	3.042758
AT2G45460	0.000142	0.046512	0.002736	3	3.049390
BRH1	0.000141	0.094391	0.035568	3	3.130100
ARF17	0.000090	0.017784	0.015048	3	3.032922
EDA16	0.000017	0.034200	0.035568	3	3.069784
AT5G28040	0.000017	0.019152	0.058824	3	3.077992
AT2G23780	0.000017	0.015048	0.064295	3	3.079360
GT-1	0.000017	0.016416	0.062927	3	3.079360
TLP10	0.000017	0.025992	0.050616	3	3.076624
ARF11	0.000002	0.036936	0.102599	3	3.139537

Search for individual genes

gene = 'SHR'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	end_degree_centrality	end_out_centrality	end_in_centrality	end_closeness_centrality	end_eigenvector_centrality	per_degree_centrality	per_out_centrality	per_in_centrality	per_betweenness_centrality	per_closeness_centrality	per_eigenvector_centrality	pro_degree_centrality	pro_out_centrality	pro_in_centrality	pro_closeness_centrality	pro_eigenvector_centrality
SHR	1.0	0.021858	0.004372	0.017486	0.000344	0.021762	0.146226	0.055031	0.091195	0.001572	0.000366	0.052812	0.060794	0.044665	0.016129	0.000388	0.021401

gene = 'BLJ'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	cor_degree_centrality	cor_out_centrality	cor_in_centrality	cor_closeness_centrality	cor_eigenvector_centrality	end_degree_centrality	end_out_centrality	end_in_centrality	end_betweenness_centrality	end_closeness_centrality	end_eigenvector_centrality
BLJ	1.0	0.039443	0.025522	0.013921	0.000165	0.019281	0.818579	0.629508	0.189071	0.015301	0.000541	0.157907

gene = 'JKD'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	cor_degree_centrality	cor_out_centrality	cor_in_centrality	cor_betweenness_centrality	cor_closeness_centrality	cor_eigenvector_centrality	end_degree_centrality	end_out_centrality	end_in_centrality	end_betweenness_centrality	end_closeness_centrality	end_eigenvector_centrality
JKD	2.0	0.679814	0.426914	0.2529	0.035369	0.000269	0.134281	0.307104	0.169399	0.137705	0.006561	0.000525	0.092102

gene = 'RVN'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	end_degree_centrality	end_out_centrality	end_in_centrality	end_closeness_centrality	end_eigenvector_centrality
RVN	0.07541	0.00765	0.06776	0.000462	0.04864

gene = 'BIB'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	end_degree_centrality	end_out_centrality	end_in_centrality	end_closeness_centrality	end_eigenvector_centrality
BIB	0.185792	0.111475	0.074317	0.000472	0.075902

gene = 'IME'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

gene = 'MYB66'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

gene = 'GL2'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	lrc_degree_centrality	lrc_out_centrality	lrc_in_centrality	lrc_closeness_centrality	lrc_eigenvector_centrality
GL2	1.0	0.716456	0.41519	0.301266	0.985575	0.000545	0.142819	0.002597	0.001299	0.001299	0.000382	0.002241

tf_spec.to_csv('TF_GRN_centrality_t3-t5_zscore3.csv', index=True)